




import warnings

from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum

from ...processing_utils import ProcessorMixin


if is_torch_available():
     import torch





class 					__lowerCamelCase			(       UpperCamelCase__       ):




    """simple docstring"""

    snake_case__   = "char"
    snake_case__   = "bpe"
    snake_case__   = "wp"






UpperCamelCase								=	(DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)





class 					__lowerCamelCase			(       UpperCamelCase__       ):




    """simple docstring"""

    snake_case__   = ["image_processor", "char_tokenizer"]
    snake_case__   = "ViTImageProcessor"
    snake_case__   = "MgpstrTokenizer"


    def __init__(			self	:	Any  ,      SCREAMING_SNAKE_CASE__	:	Dict=None  ,      SCREAMING_SNAKE_CASE__	:	int=None  ,      **SCREAMING_SNAKE_CASE__	:	str  ) ->			str:
         lowerCAmelCase__            =					None
         if "feature_extractor" in kwargs:
              warnings.warn(
                  "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
                  " instead."  ,      SCREAMING_SNAKE_CASE__  ,      )
              lowerCAmelCase__            =					kwargs.pop("feature_extractor"  )

         lowerCAmelCase__            =					image_processor if image_processor is not None else feature_extractor
         if image_processor is None:
              raise ValueError("You need to specify an `image_processor`."  )
         if tokenizer is None:
              raise ValueError("You need to specify a `tokenizer`."  )

         lowerCAmelCase__            =					tokenizer
         lowerCAmelCase__            =					AutoTokenizer.from_pretrained("gpt2"  )
         lowerCAmelCase__            =					AutoTokenizer.from_pretrained("bert-base-uncased"  )

         super().__init__(SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  )


    def __call__(			self	:	List[str]  ,      SCREAMING_SNAKE_CASE__	:	List[str]=None  ,      SCREAMING_SNAKE_CASE__	:	Any=None  ,      SCREAMING_SNAKE_CASE__	:	Union[str, Any]=None  ,      **SCREAMING_SNAKE_CASE__	:	Dict  ) ->			Dict:
         if images is None and text is None:
              raise ValueError("You need to specify either an `images` or `text` input to process."  )

         if images is not None:
              lowerCAmelCase__            =					self.image_processor(SCREAMING_SNAKE_CASE__  ,      return_tensors=SCREAMING_SNAKE_CASE__  ,      **SCREAMING_SNAKE_CASE__  )
         if text is not None:
              lowerCAmelCase__            =					self.char_tokenizer(SCREAMING_SNAKE_CASE__  ,      return_tensors=SCREAMING_SNAKE_CASE__  ,      **SCREAMING_SNAKE_CASE__  )

         if text is None:
              return inputs
         elif images is None:
              return encodings
         else:
              lowerCAmelCase__            =					encodings["input_ids"]
              return inputs


    def   a (			self	:	Optional[int]  ,      SCREAMING_SNAKE_CASE__	:	List[str]  ) ->			Any:
         lowerCAmelCase__      ,						lowerCAmelCase__      ,						lowerCAmelCase__            =					sequences
         lowerCAmelCase__            =					char_preds.size(0  )

         lowerCAmelCase__      ,						lowerCAmelCase__            =					self._decode_helper(SCREAMING_SNAKE_CASE__  ,      "char"  )
         lowerCAmelCase__      ,						lowerCAmelCase__            =					self._decode_helper(SCREAMING_SNAKE_CASE__  ,      "bpe"  )
         lowerCAmelCase__      ,						lowerCAmelCase__            =					self._decode_helper(SCREAMING_SNAKE_CASE__  ,      "wp"  )

         lowerCAmelCase__            =					[]
         lowerCAmelCase__            =					[]
         for i in range(SCREAMING_SNAKE_CASE__  ):
              lowerCAmelCase__            =					[char_scores[i], bpe_scores[i], wp_scores[i]]
              lowerCAmelCase__            =					[char_strs[i], bpe_strs[i], wp_strs[i]]
              lowerCAmelCase__            =					scores.index(max(SCREAMING_SNAKE_CASE__  )  )
              final_strs.append(strs[max_score_index]  )
              final_scores.append(scores[max_score_index]  )

         lowerCAmelCase__            =					{}
         lowerCAmelCase__            =					final_strs
         lowerCAmelCase__            =					final_scores
         lowerCAmelCase__            =					char_strs
         lowerCAmelCase__            =					bpe_strs
         lowerCAmelCase__            =					wp_strs
         return out


    def   a (			self	:	List[Any]  ,      SCREAMING_SNAKE_CASE__	:	Tuple  ,      SCREAMING_SNAKE_CASE__	:	Tuple  ) ->			str:
         if format == DecodeType.CHARACTER:
              lowerCAmelCase__            =					self.char_decode
              lowerCAmelCase__            =					1
              lowerCAmelCase__            =					"[s]"
         elif format == DecodeType.BPE:
              lowerCAmelCase__            =					self.bpe_decode
              lowerCAmelCase__            =					2
              lowerCAmelCase__            =					"#"
         elif format == DecodeType.WORDPIECE:
              lowerCAmelCase__            =					self.wp_decode
              lowerCAmelCase__            =					102
              lowerCAmelCase__            =					"[SEP]"
         else:
              raise ValueError(f'Format {format} is not supported.'  )

         lowerCAmelCase__      ,						lowerCAmelCase__            =					[], []
         lowerCAmelCase__            =					pred_logits.size(0  )
         lowerCAmelCase__            =					pred_logits.size(1  )
         lowerCAmelCase__      ,						lowerCAmelCase__            =					pred_logits.topk(1  ,      dim=-1  ,      largest=SCREAMING_SNAKE_CASE__  ,      sorted=SCREAMING_SNAKE_CASE__  )
         lowerCAmelCase__            =					preds_index.view(-1  ,      SCREAMING_SNAKE_CASE__  )[:, 1:]
         lowerCAmelCase__            =					decoder(SCREAMING_SNAKE_CASE__  )
         lowerCAmelCase__      ,						lowerCAmelCase__            =					torch.nn.functional.softmax(SCREAMING_SNAKE_CASE__  ,      dim=2  ).max(dim=2  )
         lowerCAmelCase__            =					preds_max_prob[:, 1:]

         for index in range(SCREAMING_SNAKE_CASE__  ):
              lowerCAmelCase__            =					preds_str[index].find(SCREAMING_SNAKE_CASE__  )
              lowerCAmelCase__            =					preds_str[index][:pred_eos]
              lowerCAmelCase__            =					preds_index[index].cpu().tolist()
              lowerCAmelCase__            =					pred_index.index(SCREAMING_SNAKE_CASE__  ) if eos_token in pred_index else -1
              lowerCAmelCase__            =					preds_max_prob[index][: pred_eos_index + 1]
              lowerCAmelCase__            =					pred_max_prob.cumprod(dim=0  )[-1] if pred_max_prob.nelement() != 0 else 0.0
              dec_strs.append(SCREAMING_SNAKE_CASE__  )
              conf_scores.append(SCREAMING_SNAKE_CASE__  )

         return dec_strs, conf_scores


    def   a (			self	:	Union[str, Any]  ,      SCREAMING_SNAKE_CASE__	:	Any  ) ->			Dict:
         lowerCAmelCase__            =					[seq.replace(" "  ,      ""  ) for seq in self.char_tokenizer.batch_decode(SCREAMING_SNAKE_CASE__  )]
         return decode_strs


    def   a (			self	:	Any  ,      SCREAMING_SNAKE_CASE__	:	int  ) ->			Optional[int]:
         return self.bpe_tokenizer.batch_decode(SCREAMING_SNAKE_CASE__  )


    def   a (			self	:	Tuple  ,      SCREAMING_SNAKE_CASE__	:	str  ) ->			List[Any]:
         lowerCAmelCase__            =					[seq.replace(" "  ,      ""  ) for seq in self.wp_tokenizer.batch_decode(SCREAMING_SNAKE_CASE__  )]
         return decode_strs








'''simple docstring'''







import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu


__lowerCAmelCase        =							False




class   __SCREAMING_SNAKE_CASE       (unittest.TestCase       ):


  """simple docstring"""


  def    _a       (			self	):






         """simple docstring"""


         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()







  @property
  def    _a       (			self	):






         """simple docstring"""


         return 12







  @property
  def    _a       (			self	):






         """simple docstring"""


         return 12







  @property
  def    _a       (			self	):






         """simple docstring"""


         return 32







  @property
  def    _a       (			self	):






         """simple docstring"""


         torch.manual_seed(0	)
         a_						     =			VQModel(
             block_out_channels=[32, 64]   ,	in_channels=3   ,	out_channels=3   ,	down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']   ,	up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']   ,	latent_channels=3   ,	num_vq_embeddings=self.num_embed   ,	vq_embed_dim=3   ,	)
         return model







  @property
  def    _a       (			self	):






         """simple docstring"""


         a_						     =			CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'	)
         return tokenizer







  @property
  def    _a       (			self	):






         """simple docstring"""


         torch.manual_seed(0	)
         a_						     =			CLIPTextConfig(
             bos_token_id=0   ,	eos_token_id=2   ,	hidden_size=self.text_embedder_hidden_size   ,	intermediate_size=37   ,	layer_norm_eps=1e-05   ,	num_attention_heads=4   ,	num_hidden_layers=5   ,	pad_token_id=1   ,	vocab_size=1_000   ,	)
         return CLIPTextModel(UpperCamelCase__	)







  @property
  def    _a       (			self	):






         """simple docstring"""


         torch.manual_seed(0	)

         a_						     =			12
         a_						     =			12

         a_						     =			{
             'attention_bias': True,
             'cross_attention_dim': 32,
             'attention_head_dim': height * width,
             'num_attention_heads': 1,
             'num_vector_embeds': self.num_embed,
             'num_embeds_ada_norm': self.num_embeds_ada_norm,
             'norm_num_groups': 32,
             'sample_size': width,
             'activation_fn': 'geglu-approximate',
         }

         a_						     =			TransformeraDModel(**UpperCamelCase__	)
         return model







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			'cpu'

         a_						     =			self.dummy_vqvae
         a_						     =			self.dummy_text_encoder
         a_						     =			self.dummy_tokenizer
         a_						     =			self.dummy_transformer
         a_						     =			VQDiffusionScheduler(self.num_embed	)
         a_						     =			LearnedClassifierFreeSamplingEmbeddings(learnable=UpperCamelCase__	)

         a_						     =			VQDiffusionPipeline(
             vqvae=UpperCamelCase__   ,	text_encoder=UpperCamelCase__   ,	tokenizer=UpperCamelCase__   ,	transformer=UpperCamelCase__   ,	scheduler=UpperCamelCase__   ,	learned_classifier_free_sampling_embeddings=UpperCamelCase__   ,	)
         a_						     =			pipe.to(UpperCamelCase__	)
         pipe.set_progress_bar_config(disable=UpperCamelCase__	)

         a_						     =			'teddy bear playing in the pool'

         a_						     =			torch.Generator(device=UpperCamelCase__	).manual_seed(0	)
         a_						     =			pipe([prompt]   ,	generator=UpperCamelCase__   ,	num_inference_steps=2   ,	output_type='np'	)
         a_						     =			output.images

         a_						     =			torch.Generator(device=UpperCamelCase__	).manual_seed(0	)
         a_						     =			pipe(
             [prompt]   ,	generator=UpperCamelCase__   ,	output_type='np'   ,	return_dict=UpperCamelCase__   ,	num_inference_steps=2	)[0]

         a_						     =			image[0, -3:, -3:, -1]
         a_						     =			image_from_tuple[0, -3:, -3:, -1]

         assert image.shape == (1, 24, 24, 3)

         a_						     =			np.array([0.6_551, 0.6_168, 0.5_008, 0.5_676, 0.5_659, 0.4_295, 0.6_073, 0.5_599, 0.4_992]	)

         assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-2
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice	).max() < 1e-2







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			'cpu'

         a_						     =			self.dummy_vqvae
         a_						     =			self.dummy_text_encoder
         a_						     =			self.dummy_tokenizer
         a_						     =			self.dummy_transformer
         a_						     =			VQDiffusionScheduler(self.num_embed	)
         a_						     =			LearnedClassifierFreeSamplingEmbeddings(
             learnable=UpperCamelCase__   ,	hidden_size=self.text_embedder_hidden_size   ,	length=tokenizer.model_max_length	)

         a_						     =			VQDiffusionPipeline(
             vqvae=UpperCamelCase__   ,	text_encoder=UpperCamelCase__   ,	tokenizer=UpperCamelCase__   ,	transformer=UpperCamelCase__   ,	scheduler=UpperCamelCase__   ,	learned_classifier_free_sampling_embeddings=UpperCamelCase__   ,	)
         a_						     =			pipe.to(UpperCamelCase__	)
         pipe.set_progress_bar_config(disable=UpperCamelCase__	)

         a_						     =			'teddy bear playing in the pool'

         a_						     =			torch.Generator(device=UpperCamelCase__	).manual_seed(0	)
         a_						     =			pipe([prompt]   ,	generator=UpperCamelCase__   ,	num_inference_steps=2   ,	output_type='np'	)
         a_						     =			output.images

         a_						     =			torch.Generator(device=UpperCamelCase__	).manual_seed(0	)
         a_						     =			pipe(
             [prompt]   ,	generator=UpperCamelCase__   ,	output_type='np'   ,	return_dict=UpperCamelCase__   ,	num_inference_steps=2	)[0]

         a_						     =			image[0, -3:, -3:, -1]
         a_						     =			image_from_tuple[0, -3:, -3:, -1]

         assert image.shape == (1, 24, 24, 3)

         a_						     =			np.array([0.6_693, 0.6_075, 0.4_959, 0.5_701, 0.5_583, 0.4_333, 0.6_171, 0.5_684, 0.4_988]	)

         assert np.abs(image_slice.flatten() - expected_slice	).max() < 2.0
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice	).max() < 1e-2





@slow
@require_torch_gpu
class   __SCREAMING_SNAKE_CASE       (unittest.TestCase       ):


  """simple docstring"""


  def    _a       (			self	):






         """simple docstring"""


         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			load_numpy(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy'	)

         a_						     =			VQDiffusionPipeline.from_pretrained('microsoft/vq-diffusion-ithq'	)
         a_						     =			pipeline.to(UpperCamelCase__	)
         pipeline.set_progress_bar_config(disable=UpperCamelCase__	)

         # requires GPU generator for gumbel softmax
         # don't use GPU generator in tests though
         a_						     =			torch.Generator(device=UpperCamelCase__	).manual_seed(0	)
         a_						     =			pipeline(
             'teddy bear playing in the pool'   ,	num_images_per_prompt=1   ,	generator=UpperCamelCase__   ,	output_type='np'   ,	)

         a_						     =			output.images[0]

         assert image.shape == (256, 256, 3)
         assert np.abs(expected_image - image	).max() < 2.0




